Managing extreme risks in tranquil and volatile markets using conditional extreme value theory

Financial risk management typically deals with low-probability events in the tails of asset price distributions. To capture the behavior of these tails, one should therefore rely on models that explicitly focus on the tails. Extreme value theory (EVT)-based models do exactly that, and in this paper, we apply both unconditional and conditional EVT models to the management of extreme market risks in stock markets. We find conditional EVT models to give particularly accurate Value-at-Risk (VaR) measures, and a comparison with traditional (Generalized ARCH (GARCH)) approaches to calculate VaR demonstrates EVT as being the superior approach both for standard and more extreme VaR quantiles.     

Electricity spot price modelling with a view towards extreme spike risk

Sums of Lévy-driven Ornstein–Uhlenbeck processes are appropriate for modelling electricity spot price data. In this paper we present a new estimation method with particular emphasis on capturing the high peaks, which is one of the stylized features of such data. After introducing our method we show it at work for the EEX Phelix Base electricity price index. We also present a small simulation study to demonstrate the performance of our estimation procedure.     

Financial hedging with derivatives and its impact on the Colombian market value for listed companies

The financial theory (Modigliani & Miller, 1958) presents risk management as a matter without importance in companies, given that the shareholders themselves managed their hedges, diversifying their portfolios. However, subsequent studies dispute said premise and present evidence that business financial hedging improves performance and increases the value of the same (Ahmed, Azevedo, & Guney, 2014; Allayannis & Weston, 2001; Kapitsinas, 2008). The efficient market risk management is supported in the financial derivatives, and demands strategic and efficient administrators in hedges that add value, especially in the face of clashes and macroeconomic and financial imbalances. The empirical evidence analyzes the behavior of the Q-Tobin as an indicator of the effect of the hedge strategies for the exchange rate associated to the market value. The aim of this work is to find evidence in Colombia on the effect of the use of derivatives in the market value of the company. Its added value lies in the analysis that is done by economic sectors, identified by ISIC codes and grouped into five (5) macro sectors (Agriculture and livestock, Commercial, Industrial or Manufacture, Services, and Construction). The methodology used includes the estimation of several regression models in data panels, using a Pooled regression model with fixed and random effect estimators through the maximum likelihood estimator. In general, a statistical and financially significant premium for hedges was found for companies exposed to exchange rate risks that use derivatives of a 6.3% average on the market value. Additionally, mixed results were found in relation to the variables analyzed in the model.     

Capturing downside risk in financial markets: the case of the Asian Crisis

Using data on Asian equity markets, we observe that during periods of financial turmoil, deviations from the mean-variance framework become more severe, resulting in periods with additional downside risk to investors. Current risk management techniques failing to take this additional downside risk into account will underestimate the true Value-at-Risk with greater severity during periods of financial turnoil. We provide a conditional approach to the Value-at-Risk methodology, known as conditional VaR-x, which to capture the time variation of non-normalities allows for additional tail fatness in the distribution of expected returns. These conditional VaR-x estimates are then compared to those based on the RiskMetrics™ methodology from J.P. Morgan, where we find that the model provides improved forecasts of the Value-at-Risk. We are therefore able to show that our conditional VaR-x estimates are better able to capture the nature of downside risk, particularly crucial in times of financial crises.     

Change analysis of a dynamic copula for measuring dependence in multivariate financial data

This paper proposes a new approach to measure dependencies in multivariate financial data. Data in finance and insurance often cover a long time period. Therefore, the economic factors may induce some changes within the dependence structure. Recently, two methods have been proposed using copulas to analyse such changes. The first approach investigates changes within the parameters of the copula. The second determines the sequence of copulas using moving windows. In this paper we take into account the non-stationarity of the data and analyse the impact of (1) time-varying parameters for a copula family, and (2) the sequence of copulas, on the computations of the VaR and ES measures. We propose tests based on conditional copulas and the goodness-of-fit to decide the type of change, and further give the corresponding change analysis. We illustrate our approach using the Standard & Poor 500 and Nasdaq indices in order to compute risk measures using the two previous methods.     

Testing for differences in the tails of stock-market returns

In this paper, we use a database consisting of daily stock-market returns for 20 countries to test for similarities between the left and right tails of returns, as well as across countries. We estimate and test using the distribution of extreme returns over subsamples approach. Via Monte-Carlo simulations, we show that maximum-likelihood estimators are essentially unbiased, provided the size of subsamples is correctly chosen, and that the likelihood-ratio tests on parameters characterizing the behavior of extremes are correctly sized. For actual returns, we find that left and right tails behave very similarly. Across countries, we find that extremes are located at different levels and that their dispersion varies. The tail index, characterizing large extreme realizations, is found to be constant within each geographical group. We verify that the perception that left tails are heavier than right ones is not due to clustering of extremes. The failure to detect statistical significant differences is likely to be due to the relative infrequency of large extremes.     

基于条件极值分布的金融高频数据VaR动态估计模型

高频数据由于自身数量大、周期短、信息丰富的特点而受到关注。基于高频数据。对金融时间序列的厚尾特征进行条件极值分布下的VaR估计。在对条件均值和条件波动率估计时,以往采用一阶自回归模型和GARCH模型,但基于高频数据的估计较为繁复。为了充分利用日内信息,基于高频样本观测值,建立已实现均值RM模型,在考虑市场异质性的基础上,对条件均值进行估计。通过对TCL股票价格进行实证分析,估计出VaR风险值,验证模型是合理的。     

An analysis of real-estate risk using the present value model

The current study uses a present value model that allows for a time-varying expected discount rate in conjunction with a VAR process to decompose real-estate risk. The study finds that the variance ofunexpected returns accounts for most of the total risk with cash-flow risk accounting for twice as much of the unexplained real-estate risk although discount rate risk is also an important factor. This dominance of cash-flow risk is found to result in a weaker mean reversion process for real estate relative to stocks. Another finding is that real estate investors tend to become apprehensive about the future when news on future cash flow is good, and thus they demand higher expected future returns.     

Decoupled earnings: An institutional perspective of the consequences of maximizing shareholder value

Recent accounting scandals have brought focus on the role of management in financial statement manipulation. This focus on micro-behavior does not capture the complexities of earnings management. Taking an institutional rather than agency theory approach, earnings management is posited as a decoupled behavior. A behavior that results from not only agency-based motivations of self-interests, but also regulative, normative, and cultural-cognitive legitimacy pressures. Conformity to the central logic of “maximizing shareholder value” found in the “US financial market” institutional field provides the context in which to explore earnings management as a decoupled behavior. Insights for earnings management include the blending of agency and institutional theory perspectives to gain a more complete understanding of the behavior and the positing of a continuum of earnings management conducive to this merger. Institutional theory benefits from exploring the nesting in multiple institutional fields.     

On Some alternative estimates of the adjustment coefficient in risk theory

Abstract

Recently, Csörgö and Steinebach proposed to estimate the adjustment coefficient in risk theory via a quantile type estimate based upon a sequence of intermediate order statistics. In the present paper, further alternative estimators are discussed which may be viewed as convex combinations of a Hill type and a quantile type estimate. Consistency is proved and rates of convergence are studied. Some simulation results are presented to illustrate the finite sample behavior of the proposed estimators.     

Evaluating disclosure theory using the views of UK finance directors in the intellectual capital context

In contrast to most prior research in this area, which focuses on actual disclosures, this study uses a large-scale direct method to investigate the factors that a key preparer group believes influence intellectual capital (IC) disclosure decisions. IC disclosures are typically characterised by uncertainty of interpretation and high levels of commercial sensitivity. A questionnaire elicits 93 UK-listed company finance directors' views regarding the influences on these decisions. Results are used to evaluate the relative explanatory power of several theoretical and practical reasons for disclosure. Strongest support is found for competitive disadvantage and capital market considerations. Issues related to legitimacy theory, stakeholder theory and other economic disclosure costs also feature. Factor analysis reduces the set of 28 incentives and disincentives to 10 uncorrelated dimensions, indicating that a broad and complex set of overlapping factors affect the disclosure decision. The importance of disclosure incentives and disincentives is found to vary both within and between disclosure topics, which may explain the variation in findings in prior research.     

A note on different models of stochastic processes dealt with in the collective theory of risk

Abstract

1. For the definition of general processes with special regard to those concerned in Collective Risk Theory reference is made to Cramér (Collective Risk Theory, Skandia Jubilee Volume, Stockholm, 1955). Let the independent parameter of such a process be denoted by τ, with the origin at the point of departure of the process and on a scale independent of the number of expected changes of the random function. Denote with p(τ, n)dt the asymptotic expression for the conditional probability of one change in the random function while the parameter passes from τ to τ + dτ: relative to the hypothesis that n changes have occurred, while the parameter passes from 0 to τ. Assume further—unless the contrary is stated—that the probability of more than one change, while the parameter passes from τ to τ + dτ, is of smaller order than dτ.     

The influence of the CEO's value perception towards auditing on audit demand in private firms

Audit demand is generally considered to be a direct reflection of the level of agency conflicts. This study examines the CEO's value perception towards auditing as additional driver for both auditor reputation demand (appointing a Big 4 auditor or not) and audit effort demand in private firms. Examining the CEO's value perception in a multidimensional way, the regression results indicate that the CEO's functional value perception towards auditing positively affects the demand for audit effort, while the CEO's social value perception towards auditing negatively affects the demand for audit effort but positively affects the demand for auditor reputation.